Open AccessGlobal monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base
Author(s) -
Christina M Astley,
Gaurav Tuli,
Kimberly A. Mc Cord,
Emily Cohn,
Benjamin Rader,
Tanner J Varrelman,
Samantha Chiu,
Xiaoyi Deng,
Kathleen Stewart,
Tamer H. Farag,
K. M. Barkume,
Sarah LaRocca,
Katherine Morris,
Frauke Kreuter,
John S. Brownstein
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2111455118
Subject(s) - covid-19 , demographics , pandemic , sampling (signal processing) , government (linguistics) , survey data collection , environmental health , computer science , geography , demography , medicine , statistics , telecommunications , disease , mathematics , pathology , virology , outbreak , infectious disease (medical specialty) , linguistics , philosophy , sociology , detector
Significance The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 2020, is the largest remote global health monitoring system. This study includes ∼30 million responses through December 2020 from all 114 countries/territories with survey weights to adjust for nonresponse and demographics. Using self-reported cross-sectional survey data sampled daily from Facebook users, we confirm consistent demographics and COVID-19 symptoms. Our global model predicts local COVID-19 case trends. Importantly, one survey item strongly correlates with reported cases, demonstrating potential utility in locales with scant UMD-CTIS sampling or government data. Despite limitations resulting from sampling, nonresponse, coverage, and measurement error, UMD-CTIS has the potential to support existing monitoring systems for COVID-19 as well as other new as-yet-undefined global health threats.